Michel Ghorayeb | Managing Director | SAS UAE | mail me |
Financial leaders are now being asked to fund Artificial Intelligence (AI), govern it, and explain the value it creates. This isn’t a simple technology question. In banking, it is becoming a test of leadership.
AI is starting to influence decisions at the core of the business. These include credit, fraud, pricing, collections, customer engagement, compliance and operational resilience. These decisions affect customers, regulators, shareholders, employees and the institution’s ability to compete.
Banks don’t need faster AI; they need discipline
Speed alone will not define the next phase of AI in financial services. Banks will need discipline, especially as AI begins to influence decisions that customers and regulators may later challenge.
The investment is already happening. Our Data and AI Impact Report, with research insights from IDC, found that banks are ahead of other sectors in AI spending and in the adoption of trustworthy AI practices. That sounds encouraging, and it is, up to a point. The same research also shows that a trust gap still exists.
Only 11% of banks have both high internal confidence in AI and systems that are demonstrably trustworthy. Nearly half fall into what IDC describes as the “trust dilemma”. They either underuse reliable AI because they do not trust it enough or over-rely on AI that has not been properly validated.
Governance considerations
Confidence can be misleading. A model can perform well in testing and still fail the institution if the data is fragmented, governance is weak, or no one can explain how the decision-making process took place. Banking is not forgiving terrain for unclear decisions.
The problem usually shows up in ordinary places. It may involve a credit recommendation that cannot be properly traced, a fraud model that produces too many false positives, or a customer receiving inconsistent treatment across channels. The issue is not only whether the model works. It is also whether the bank can explain, monitor, and adjust the decisions it supports.
Governance cannot serve as the final stamp of approval at the end of the innovation process. It has to form part of how AI teams design, deploy and manage systems from the start. This does not mean slowing everything down.
In many cases, good governance helps organisations move faster because teams know the rules. They know which data they can use, which models need review, which decisions require human oversight, and where escalation is needed. In a regulated environment, that clarity allows innovation to survive real customers, regulators and market pressure. In other words, banks don’t need faster AI if they cannot govern its decisions effectively.
Getting value
Return on investment (ROI) needs the same discipline. Efficiency matters, and banks cannot ignore cost pressure. But the stronger AI business case is not always found in replacing effort. It is often used to improve the quality, speed and consistency of decisions.
The banking findings support this. Organisations using AI to improve customer experience reported stronger returns than those focused primarily on cost savings. The study also found that organisations prioritising trustworthy AI were 60% more likely to report doubling overall return on their AI initiatives.
Instead of asking only what can be automated, banks should ask where better decisions can create measurable value. This includes faster and fairer credit assessment and more accurate fraud detection, among other benefits.
For African financial institutions, the stakes are immediate. Banks are operating in markets shaped by digital acceleration, regulatory scrutiny, fraud pressure, affordability constraints, legacy systems, siloed data and rising customer expectations. AI has a role to play, but only if the environment around it is strong enough to support it.
An integrated approach
This is where financial leaders play a critical role. AI cannot belong only to data science teams or technology functions. It has to connect with finance, risk, compliance, operations and customer strategy.
The banks that make progress will bring these functions closer together around shared decision-making. Agentic AI makes this even more important. AI agents can analyse data, make decisions, and take action across workflows with limited human intervention.
In an enterprise setting, AI agents need more than language models. They require trusted data, advanced analytics, decision logic, governance and compliance to deliver reliable, auditable outcomes.
Banks will use more advanced AI. What is less clear is whether leadership has created firm enough boundaries around where it may act, when it must escalate and who remains accountable. Banks don’t need faster AI simply for the sake of speed. They need AI that operates within clear boundaries and supports accountable decision-making.
Delivering value
For African banks, the opportunity is to build AI into the institution with discipline from the beginning. This means cleaner data, clearer rules, measurable outcomes and people who remain accountable.
AI will help banks move faster. The harder task is making sure speed does not weaken accountability. In financial services, leadership still comes down to the quality of the decisions made, the evidence behind them, and the willingness to stand behind them when it matters.
Ultimately, banks don’t need faster AI alone. They need trustworthy, governed and valuable AI that strengthens the institution while protecting the customers and stakeholders who depend on it.